Fingerprint verification method

ABSTRACT

A method for verifying that a sample image of a fingerprint is that of a designated person through comparison of sample data, generated from the sample image, with master data, generated from a master image of the designated person&#39;s fingerprint. The master and sample image have been divided into a plurality of blocks, and each block has been divided into a plurality of block areas. Each block area has a number of pixels each having an associated direction as the sample and master data. The direction associated with each pixel was determined based on pixel density partial differentials between the pixel and adjacent pixels for a plurality of directions. The direction of the minimum pixel density partial differential for a pixel is chosen as the direction for that pixel. Based on the sample and master data fingerprint verification is determined by at least one of total dispersion, cross-correlation, and distance between classes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a fingerprint verification method whichcompares a fingerprint to be examined with a stored referencefingerprint, and finds application, for example, as a security systemwhich opens a door only for registered persons.

2. Description of the Related Art

According to the conventional fingerprint verification method for asecurity system, an image including the protuberant lines of afingerprint is binarized and thinned so as to calculate a protuberantline pattern. Then branch points, end points and curvature are extractedfrom the above protuberant line pattern as the main characteristics ofthe fingerprint. Based on those characteristics, a comparison isperformed between a master image (image of a reference fingerprint) anda sample image (image of a finger-print to be examined). Since thecharacteristics appear all over the fingerprint, it was preferable forthe conventional fingerprint verification method to have a fingerprintwhich covered a wide area. Therefore, the image of the fingerprint hadto be taken by rotating the finger which included not only the frontpart of the finger but also the sides of the finger.

The protuberant lines at the lower portion of the fingerprint, i.e., ajoint part of a finger, are usually arranged in a horizontal direction;therefore, there are few characteristics. Furthermore, the image at thelower portion of the fingerprint is usually input incorrectly;therefore, it is ineffective data for fingerprint verification.Accordingly, when using such an image for fingerprint verification,accurate verification will be difficult to perform due to the vastvolume of noise in the data. Also, when using data representing thelower part of the fingerprint, the volume of input data becomes so largethat it becomes necessary to expand memory capacity.

Furthermore, when a whole fingerprint is used for fingerprintverification, it is difficult to impress the fingerprint to be examined;therefore, the time required for fingerprint verification is long.

SUMMARY OF THE INVENTION

The object of the present invention is to solve the above problem and toprovide a fingerprint verification method that realizes accuratefingerprint verification using a small volume of data.

The present invention has as a further objective providing a fingerprintverification method that greatly simplifies the steps of generatingmaster image and sample image data for use in fingerprint verification;as well as, realizing accurate fingerprint verification within a shortperiod of time.

The objectives of the present invention are achieved by a method forgenerating sample and master data for fingerprint verification and afingerprint verfication based on the generated data.

A method for generating sample and master data for fingerprintverification, according to the present invention, includes the steps ofinputting, as one of a master image and a sample image, a gray scaleimage of a finger, including a tip of the finger, the gray scale imagecomposed of pixels; determining a center of characteristics of the grayscale image; taking as an area of examination that part of the grayscale image from the center of characteristics to the tip of the finger;calculating density partial differentials in a plurality of directionsfor each pixel in the area of examination; and, determining, as the oneof the master data and the sample data, a direction of a minimum densitypartial differential for each pixel in the area of examination.

Alternatively, the area of examination can be taken as an area havingthe center of characteristics as its center, or an area having thecenter of characteristics as its center and including the fillet center.

After the determination of the master and sample image data, fingerprintverification can be performed. Fingerprint verification based on themaster and image data generated is performed by at least one of totaldispersion, cross-correlation, and distance between classes.

The fingerprint verification method of total dispersion includes thesteps of determining a dispersion within a class of each block of thesample image based on the sample data which represents a direction of afinger characteristic within a block; determining a dispersion within aclass of each block of the master image based on the master data whichrepresents a direction of a finger characteristic within a block;determining the aberration of direction between blocks of the sampleimage and master image representing a difference in a fingercharacteristic between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; comparing the total dispersion for each block to areference total dispersion; and, verifying a fingerprint as that of thedesignated person based on results from the comparisons.

The fingerprint verification method of cross-correlation includes thesteps of determining a cross-correlation between the block areas of eachblock in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; comparing the cross-correlations betweeneach block of the sample and master image to a referencecross-correlation; and, verifying a fingerprint as that of thedesignated person based on results from the comparisons.

The fingerprint verification method of dispersion between classesincludes the steps of determining a distance between classes between theblock areas of each block in the sample image and the master image basedon a number of pixels in each block area of the sample image and masterimage associated with a direction which represents a difference indirections associated with the pixels in a block area; calculating amean of the distance between classes between the block areas for eachblock of the sample and master data to determine a distance betweenclasses between each block of the sample and master data; comparing thedistance between classes between each block of the sample and masterdata to a reference distance between classes; and, verifying afingerprint as that of the designated person based on results from thecomparisons.

Alternatively, any combination of the above methods of fingerprintverification can also be used.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a fingerprint divided into a plurality of blocks;

FIG. 2 shows a block diagram of a fingerprint verification system;

FIG. 3 shows an enlarged diagram of the slanting surface of theright-angle prism in contact with the finger;

FIG. 4 shows an example of a protuberant pattern for a fingerprint;

FIG. 5 shows a histogram with respect to the change in density value ofthe pixels in area "V" in FIG. 4;

FIG. 6 shows a histogram with respect to the change in density value ofthe pixels in area "VI" in FIG. 4;

FIG. 7 shows a diagram indicating the direction of the partialdifferential;

FIG. 8 shows a diagram for explaining the calculation method of thecenter point of an image;

FIG. 9 shows a histogram with respect to the number of pixels in eachdirection code;

FlG. 10 shows a diagram defining the direction codes; and

FIG. 11 shows a diagram indicating a fillet center.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the first embodiment of the present invention is describedwith reference to the attached drawings.

FIG. 2 shows a fingerprint verification system applying an imagecomparison method of the present invention. The fingerprint verificationsystem consists of an image input system 10 and a verificationprocessing system 20. Comparison processing is performed on the imagephotographed by the image input system 10 by the verification processingsystem 20. In the image input system 10, the total reflection ofillumination incident upon a right-angle prism 11 from a luminous source12 is detected by a CCD camera 13. A slanting surface 14 of the prism 11is deemed the detection surface, where a finger FIN having a fingerprintto be detected is contacted thereon. Without contact with the fingerFIN, the whole of the illumination is totally reflected, and the imagedetected by the CCD camera 13 is all in white.

As shown in FIG. 3, the illumination at the protruded portions FIN 1does not totally reflect; and, thus, passes through the interface of theprism because the angle of refraction at the interface of the prismchanges. Therefore, the protruded portions of the fingerprint are inputto the CCD camera 13 as dark lines with gray levels.

The verification processing system 20 comprises an image processingportion 21 and a card reader 22. Master data is read when the personbeing examined inserts an ID card 27 into the card reader 22. Then thecomparison of the data input from the image input system 10 with themaster data is performed by a computer 23. The comparison results aredisplayed at a display portion 24. When the comparison results come upto standard (coincidence of the fingerprints), an actuator 25 isoperated and a door 26 is opened.

Various manners of specifying the master data are acceptable, such asinputting from a keyboard the ID number of a person being examined.

In FIG. 4, the arrows show the general directions of the protuberantlines of a fingerprint. For an index expressing those generaldirections, the inventors adapted a histogram of the direction for whicha partial differential value of the density value for each pixel becomesminimum. For example, the partial differential value of the densityvalue of each pixel in an area "V" has a frequency which becomes minimumin the direction of 45 degrees. This result is obtained because thechange in the density value of each pixel in the area "V" has a strongtendency to become minimum in the direction rotated counterclockwise by45 degrees from horizontal. Accordingly, a histogram, as shown in FIG.5, can be obtained with respect to the change in density value of pixelsin the area "V". The partial differential value of the density value ofeach pixel in an area "VI" has a frequency which becomes minimum in thedirection of 90 degrees. This result is obtained because the change indensity value of each pixel in the area "vI" has a strong tendency tobecome minimum in the direction rotated counterclockwise by 90 degreesfrom the horizontal. Accordingly, a histogram, as shown in FIG. 6, canbe obtained with respect to the change in density value of pixels in thearea "VI".

In order to obtain a histogram as described above, the followingprocessing is performed.

The density value of each pixel in an input fingerprint image iscalculated, i.e., a sample image. Meanwhile, the density values for eachpixel of the reference fingerprint image, i.e., a master image, arecalculated and stored in the memory of the computer 23.

Then partial differentiation is performed according to the density valueof each pixel among the neighborhood pixels. Here, the neighborhoodpixels are not only pixels directly adjacent to an objective pixel onwhich partial differential is performed, but also pixels distant by afew pixels from the objective pixel. As shown in FIG. 7, partialdifferentiation is performed over a range of 157.5 degrees, in thehorizontal direction (number 1), in the directions rotated by 22.5degrees in counterclockwise direction from the horizontal direction(numbers 2, 3, 4, and 5), and in the directions rotated by 22.5 degreesin clockwise direction from the horizontal direction (numbers 6, 7 and8). When x and y coordinates are determined in the horizontal andvertical directions, respectively, and the change in value of thedensity values are deemed Δ d the partial differential value z can beexpressed by: ##EQU1##

The partial differential with respect to a digital image is discrete.Especially for the distance elements, it is necessary to differentiatecomparatively long distances depending on the direction of the partialdifferentiation. However, when the distance is too long, differentiationis performed only on the mountains of a fingerprint. As a result, thecharacteristics of the protuberant lines of the fingerprint are lost.Therefore, a minimum distance value should be used to heighten theprecision in a particular direction. According to the image processingsystem 10 in FlG. 2, corrections with respect to the elements in the Ydirection are required for an image input to the CCD camera 13 for theexamination of the fingerprint from 45 degrees slantwise. The followingeffective results were obtained by using the following distances forpartial differentiation concerning the above correction;

    0° (Δx=2, Δy=0), 22.5° (Δx=2,Δy=1),

    45° (Δx=2, Δy=2), 67.5° (Δx=2,Δy=3).

When the partial differential values for each pixel are calculated ineach direction of numbers from 1 to 8 (FIG. 7), a smaller partialdifferential value is stored in a memory by comparing the previouslycalculated partial differential value and the newly calculated partialdifferential value. By storing in memory the smaller of the partialdifferential value as between each direction, the minimum partialdifferential value is stored in the memory by the time all calculationswith respect to the directions of numbers 1-8 are complete. By repeatingthe above processing for all pixels, the minimum partial differentialvalue for all pixels are calculated.

The partial differentials are performed again in each direction ofnumbers from 1 to 8 for each pixel, so as to compare those differentialvalues With the previously calculated minimum partial differentialvalue. The number (out of numbers from 1 to 8 With respect to FIG. 7),corresponding to t he direction of the newly calculated differentialvalue which coincides with the minimum partial differential value, isstored in the memory of the image processing portion. Accordingly, thedirections of the minimum partial differential values are calculated forall pixels, and the distributions of the directions of the minimumpartial differential values for a whole image can be obtained. Since thedirection elements of each area in the image are extracted directly fromthe gray level image, it is possible to make efficient use of theinformation of an original image.

Once the distribution of the directions of the minimum partialdifferential values for the sample image is obtained, grasping of thetendency of the protuberant lines of the fingerprint is possible.Therefore, fingerprint verification becomes possible by comparing thedistribution with respect to the above direction with that of the masterimage. That is, data of the direction of the minimum partialdifferential value for each pixel in the sample image is compared withthat of the corresponding pixel in the master image.

However, according to the present embodiment as described later, animage is separated into a plurality of blocks. An index is calculatedbased on the direction where the partial differential becomes theminimum according to the portion of a block above the center of thecharacteristics of the fingerprint image, so as to perform thecomparison between the sample image and the master image. This iseffective since many characteristics of the fingerprint appear at theportion of a block above the center of the characteristics.

Before the comparison is performed between the sample image and themaster image, justification of positions for those images is performed.This justification of positions is explained with referring to FIG. 8.

In FIG. 8, the protuberant lines FP of the fingerprint are arranged tomake the spiral figure, so the pixels with the same density value arearranged along this spiral. Here, the horizontal and vertical directionsare deemed the X and Y directions, respectively. When the partialdifferential is taken of the density values between adjacent pixelsalong the Y direction, the calculated change ratio of the density valueaccording to the Y direction becomes large at, for example, the area Pwhere the protuberant lines lengthen in X direction, and becomes smallat the area Q where the protuberant lines lengthen in Y direction.Therefore, a histogram with respect to the partial differential value ofthe density values in Y direction becomes a maximum at the center asindicated by the letter R. Equivalent to the above, the change ratio ofthe density values in the X direction becomes small at the area P wherethe protuberant lines are lengthened in X direction, and becomes largeat the area Q where the protuberant lines are lengthened in Y direction.Therefore, a histogram with respect to the partial differential value ofthe density values in X direction becomes a maximum at the center asindicated by the letter S.

According to the present embodiment, the maximal point (letter T) of thehistogram (letter S) with respect to the change of density values alongX direction is deemed to be a Y coordinate of a center point of theimage. Also, the maximal point (letter U) of the histogram (letter R)with respect to the change of density values along Y direction is deemedto be a X coordinate of a center point of the image. The obtained centerpoint as above is not the center of the configuration; it is the centerof the characteristics of the image (for example, the center of thespiral). Accordingly, the center point is determined as the center ofthe characteristics where the extreme value of the histogram of thedensity partial differential values occur.

As it is shown in FIG. 1, both the master image and the sample image aredivided into, for example, 15 blocks from A to O, respectively. Thejustification of positions between the sample image and the master imageis performed, so that the blocks of both images which include the centerof the characteristics obtained by the manner described above coincideeach other.

Hereinafter, the comparison judgment between the sample image and themaster image is described.

FIG. 1 shows the manner to divide a fingerprint image 41 into aplurality of blocks. This image 41 is divided into 15 blocks from A toO, each of which is further divided into 16 blocks from area a to p thatcomprise 16×16 pixels. According to the present embodiment, thecomparison between the sample image and the master image is performedusing the data with respect to the portion of blocks above the center ofcharacteristics, blocks from A to I, out of blocks from A to O.

The total dispersion for each area from a to p of each of blocks from Ato I is calculated. Fundamentally, the total dispersion is defined bythe following formula: ##EQU2## where, θ indicates an aberration angleof the direction where the partial differential value of the masterimage and the sample image becomes minimum, Vm indicates the dispersionwithin a class for each area of the master image, and Vs indicates thedispersion within a class for each area of the sample image. Accordingto the embodiment, the total dispersion RE is defined by subtracting thetotal dispersion defined by the above formula from 1. That is, the totaldispersion RE of the present embodiment is define by: ##EQU3##

Accordingly, the following tendency is obtained, that the higher theidentification ratio is the more the total dispersion increases.

In order to obtain the total dispersion defined by the above formula(1), the dispersions within a class, Vm and Vs, with respect to themaster image and the sample image are calculated beforehand. This isdescribed with reference to FlG. 9 and FlG. 10. FlG. 9 shows a histogramwith respect to the number of pixels for the direction codes. FlG. 10shows the definition of the direction codes. That is, the directiontoward the vertical direction is determined as a code "1". Based on thiscode, the number of codes are increased to "2", "3". . . s" as thedirection rotates in clockwise direction by 22.5 degrees.

First, the direction code "1" is deemed to be a center point,temporally. Then the aberrations of directions are calculated withrespect to the above direction code "1" for each direction code.Accordingly, the aberrations of directions With respect to the directioncodes "2", "3", "4", "5", "6", "7" and "8" are 1, 2, 3, 4, 3, 2, and 1(numbers in the parentheses in FIG. 9), respectively. Here, theaberrations of directions with respect to the direction codes "2" and"8", "3" and "7", "4" and "6" are equivalent. This is because theaberration of direction does not consider whether it is in clockwisedirection or in counterclockwise direction.

Next, the sum total T is calculated by multiplying the number of pixelsfor each direction code by a weight corresponding to the magnitude ofits aberration of direction. Here, when the angle of aberration ofdirection is deemed to be θ, weight is sin² θ. When there is noaberration of direction, weight is 0.0. When the aberration of directionis 1, weight is 0.15. When the aberration of direction is 2, weight is0.5. When the aberration of direction is 3, weight is 0.85. When theaberration of direction is 4, weight is 1.0. As above, the numbers ofpixels for each direction code are multiplied by weights because it isconsidered that the farther the distance from the temporary center pointbecomes, the more the number of pixels for the dispersion within a classis influenced.

The value of the obtained sum total T divided by the number of pixels,256, in the area is the dispersion within a class. This dispersionwithin a class is calculated by deeming every direction codes to be atemporary center point. Therefore, s directions within a class arecalculated for each area. Then, the minimum value of those dispersionswithin a class is calculated so as to determine this minimum value asthe dispersion within a class of that area. Also, the direction codewhich corresponds to the minimum Value of dispersion within a class isdeemed to be the direction code with respect to that area. Accordingly,the dispersions within a class and the direction codes are determinedfor each area from a to p.

The mean values of the dispersions within a class in each area from a top are calculated for each of blocks from A to I. The dispersions withina class obtained as above are dispersions within a class, Vm and Vs, ofthe master image the sample image used for the above formula (1).Equivalent to the above, the mean value of direction codes in each areafrom a to p is calculated so as to be determined as the direction codeof the blocks from A to I. This direction code is a code used for theabove formula (1), as it is described later.

Next, the aberration of direction according to the above formula (1),cos² (θ/2), is calculated. This calculation is performed by determiningas angle "θ" the angle that corresponds to the difference in directioncodes between the master image and the sample image for blocks from A toI. For example, when the difference of direction codes between themaster image and the sample image is 1, is 22.5 degrees, so that theaberration of direction is 0.96. Equivalent to the above, when thedifference is 2, θ is 45 degrees so that the aberration of direction is0.85. When the difference is 3, θ is 67.5 degrees so that the aberrationof direction is 0.69. When the difference is 4, is 90 degrees so thatthe aberration of direction is 0.5.

The total dispersion RE with respect to the blocks from A to I iscalculated from the above formula (1) by using the aberration ofdirection (dispersion between classes) obtained as above, and thedispersion within a class, Vm and Vs. This total dispersion RE changesbetween 0 and 1. The more identical the sample and the master imagesare, the more the value of the total dispersion approaches 1. Forexample, when the sample image and the master image are almost identicalwith one another, the value of total dispersion RE with respect to theblocks from A to I becomes more than 0.8. Contrary to the above, whenthe sample image and the master image are not identical, the value ofthe total dispersion RE with respect to the blocks from A to I becomesless than 0.7.

Next, the crosscorrelations are calculated with respect to the blocksfrom A to I. ##EQU4## where, COR is the crosscorrelation, x(θi) is thenumber of pixels in θi(deg) direction with respect to the sample image,and X(θi) is the number of pixels in θi(deg) with respect to the masterimage.

First the crosscorrelations, as with the total dispersion, arecalculated for each area from a to p. Then mean values of the calculatedcrosscorrelations are determined as the crosscorrelation ofcorresponding blocks from A to I. The crosscorrelation changes between 0and 1. The more identical the sample and the master images are, the morethe value of the crosscorrelation approaches 1.

Then, the distances between classes are calculated with respect to theblocks from A to I. The distance between classes is defined as thefollowing formula: ##EQU5## where, DG is the distance between classes,x(θi) is the number of pixels in θi(deg) direction with respect to thesample image, and X(θi) is the number of pixels in θi(deg) directionwith respect to the master image.

First the distances between classes, as with the crosscorrelation, arecalculated for each area from a to p. Then mean values of the calculateddistances between classes are determined as the distance between classesof corresponding blocks from A to I. The distance between classeschanges between 0 and 1. The more identical the sample and the masterimages are, the more the value of the distance between classesapproaches 0.

Accordingly, the total dispersions, the crosscorrelations and thedistances between classes with respect to the upper blocks (blocks A toI) which include the center of characteristics are calculated withrespect to both master and sample images. In order to judge whether ornot the master image and the sample image with respect to the blocksfrom A to I coincide with one another, it is necessary to have a totaldispersion of more than 0.7, a crosscorrelation of more than 0.96 and adistance between classes of less than 0.1.

When it is judged that the sample image and master image are different,the relative location between the sample image and the master image ischanged in the horizontal direction by 1 area (from a to p of FlG. 1).Then the comparison between images is performed by the same manner asdescribed above. When it is judged again that images are different, therelative location between the sample image and the master image ischanged with respect to the original location in the opposite directionto the above by 1 area. Then the comparison between images is performedby the same manner as described above. When it is still judged that thesample image and the master image are different, even after the relativelocation of the images is changed in the horizontal direction, therelative location of images is change in vertical direction by 1 areafrom the original location to perform the comparison.

The comparison between the sample image and the master image isperformed by justifying their centers of characteristics with oneanother. When those images are judged to be different, the comparisonbetween images are continuously performed by changing the relativelocation between images by 1 area to the right and left, then up anddown, successively, from the originally justified location. Accordingly,the comparison between images is performed at most 5 times by changingthe relative location to the right and left, then up and down. Whenimages are judged to be different 5 times, it is finally judged that thesample image and master image are different.

When the comparison method is applied to the fingerprint verificationsystem shown in FIG. 2, the door 26 is opened when the fingerprint to beexamined (the sample image) is judged to coincide with the referencefingerprint (the master image). When the fingerprint to be examined isjudged not to coincide with the reference fingerprint, the door 26 willnot be opened, and the system will request the person to be examined toinput his fingerprint again, or it will output the message of "NoAdmittance".

Hereafter, the record embodiment is described. In this embodiment animage is separated into a plurality of blocks. An index is calculatedbased on the minimum direction according to the block including a centerof the characteristics of the fingerprint image, so as to perform acomparison between the sample image and the master image. This iseffective since many characteristics of the fingerprint appear aroundthe neighborhood of t he center of the characteristics.

The total dispersions for each area from a to p of the block H arecalculated according to the formula (1).

The mean value of the dispersions within a class in each area from a top is calculated. This mean value is determined as the dispersion withina class with respect to a block H. The dispersions within a classobtained as above are dispersions within a class, Vm and Vs, of themaster image and the sample image used in the above formula (1).Equivalent to the above, the mean value of direction codes in each areafrom a to p is calculated to determine the direction code of the blockH. This direction code is used for the above formula (1), as describedlater.

Next, the aberration of direction according to the above formula (1),cos² (θ/2), is calculated. This calculation is performed by determiningthe angle "θ" that corresponds to the difference of direction codesbetween the master image and the sample image according to the block H.For example, when the difference of direction codes between the masterimage and the sample image is 1, θ is 22.5 degrees, so that theaberration of direction is 0.96. Equivalent to the above, when thedifference is 2, θ is 45 degrees so that the aberration of direction is0.85. When the difference is 3, θ is 67.5 degrees so that the aberrationof direction is 0.69. When the difference is 4, θ is 90 degrees so thatthe aberration of direction is 0.5.

The total dispersion RE with respect to the block H is calculated fromthe above formula (1), using the aberration of direction (dispersionbetween classes) obtained above, and the dispersion within a class, Vmand Vs. This total dispersion RE changes between 0 and 1. The moreidentical the sample and the master images are, the more the value ofthe total dispersion approaches 1. For example, when the sample imageand the master image are almost identical With one another, the value oftotal dispersion RE with respect to the block H becomes more than 0.8.Contrary to the above, when the sample image and the master image arenot identical, the Value of the total dispersion RE with respect to theblock H becomes less than 0.7.

Next, the crosscorrelation is calculated with respect to the block H.

First the crosscorrelations, as with the total dispersion, arecalculated for each area from a to p. Then a mean value of thecalculated crosscorrelations is determined as the crosscorrelation ofcorresponding block H. The crosscorrelation changes between 0 and 1. Themore identical the sample and the master images are, the more the valueof the crosscorrelation approaches 1.

Then, the distance between classes is calculated With respect to theblock H.

First this distance between classes, as with the crosscorrelation, arecalculated for each area from a to p. Then a mean value of thecalculated distances between classes is determined as the distancebetween classes of corresponding block H.

Accordingly, the total dispersion, the crosscorrelation and the distancebetween classes of the block H are calculated with respect to bothmaster and sample images, which include the center of characteristics.In order to judge whether or not the master image and the sample imagecoincide with each other, it is necessary to have a total dispersion ofmore than 0.7, a crosscorrelation of more than 0.96 and a distancebetween classes of less than 0.1.

Hereinafter, two other embodiments according to the present inventionare described. First, according to FIG. 1, it is possible to performfingerprint verification using data of not only the block H, whichincludes the center of characteristics, but also blocks adjacent to theblock H (for example, 1 or more than 1 block from D, E, F, G, 1, J, Kand L).

Second, according to FIG. 11, it is possible to perform fingerprintverification using only data with respect to a block H'. Here, the blockH' includes the fillet center Z of the rectangle W surrounding thefingerprint image that is determined to be the Center of thecharacteristics. Furthermore, it is possible to perform fingerprintverification using data in the block H' including the fillet center andin adjacent blocks to the block H'.

For fingerprint verification, it is not necessary to use all theindices, that is, the total dispersion, the crosscorrelation, and thedistance between classes. The comparison between images can be performedby using one or two indices from the above.

As mentioned above, it is possible to realize accurate fingerprintverification using a small volume of data.

As mentioned above, it is possible to greatly simplify the steps ofgenerating a master image and a sample image used for fingerprintverification, as well as realizing accurate fingerprint verificationwithin a short period of time.

I claim:
 1. A method for verifying that a sample image of a fingerprintis that of a designated person through comparison of sample data,generated from the sample image, with master data, generated from amaster image of the designated person's fingerprint, the master andsample image having been divided into a plurality of blocks and eachblock having been divided into a plurality of block areas, each blockarea is comprised of pixels each having an associated direction as thesample and master data, the method comprising the steps of:determining adispersion within a class of each block of the sample image based on thesample data which represents a direction of a finger characteristicwithin a block; determining a dispersion within a class of each block ofthe master image based on the master data which represents a directionof a finger characteristic within a block; determining the aberration ofdirection between blocks of the sample image and master imagerepresenting a difference in a finger characteristic between respectiveblocks of the sample image and master image based on the sample data andmaster data; determining a total dispersion based on the dispersionwithin a class for each block of the sample image and the master imageand the aberration of direction which represents a measure of thesimilarity between the sample image and the master image; comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the comparisons.
 2. The method as in claim 1, wherein thesteps of determining the dispersions within a class for each block ofthe sample image and master image comprises the steps of:determining anaberration of direction between a plurality of direction codes and adirection of a pixel for each pixel in each block area which representsa difference in direction between a direction of a pixel and a directioncode; determining a dispersion within a class of each block area foreach direction code, for each direction code the determination of adispersion within a class of each block area comprises the stepsof,multiplying each pixel within a block area by a weight to form apixel product, the weight corresponding to the aberration of directionbetween the pixel and the direction code; summing the pixel products ofthe pixels in the block area; dividing the summed pixel products for theblock area by the number of pixels in the block area to determine adispersion within a class of the block area for that direction code;determining as the dispersion within a class of a block area a one ofthe dispersions within a class of the block area for each direction codewhich is a minimum; summing as a block dispersion sum, the dispersionswithin a class of a block area for a block; and dividing the block sumof a block by a number of block areas in the block to determine thedispersion within a class of the block.
 3. The method as in claim 2,wherein the step of determining the aberration of direction comprisesthe steps of:providing for each block area of the sample and masterimage a direction associated with a fingerprint characteristic of ablock area, the provided direction being the direction code for whichthe dispersion within a class of the block area for the direction codeswas a minimum; determining a direction, which is associated with afingerprint characteristic of a block, for each block in the sample andmaster image by calculating the mean of the provided directions of eachblock area within a respective block; determining an aberration ofdirection between corresponding blocks of the sample and master imagebased on the determined directions of the blocks in the sample andmaster image.
 4. The method of claim 1, wherein the step of determiningthe aberration of direction comprises the steps of:determining adirection, which is associated with a fingerprint characteristic of ablock area, for each block area in a block for both the sample andmaster image based on the sample and master data; determining adirection, which is associated with a fingerprint characteristic of ablock, for each block in the sample and master image by calculating themean of the directions of each block area within a respective block;determining an aberration of direction between corresponding blocks ofthe sample and master image based on the determined directions of theblocks in the sample and master image.
 5. A method for verifying that asample image of a fingerprint is that of a designated person throughcomparison of sample data, generated from the sample image, with masterdata, generated from a master image of the designated person'sfingerprint, the master and sample image having been divided into aplurality of blocks and each block having been divided into a pluralityof block areas, each block area is comprised of pixels each having anassociated direction as the sample and master data, the methodcomprising the steps of:determining a dispersion within a class of eachblock of the sample image based on the sample data which represents adirection of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in afinger characteristic between respective blocks of the sample image andmaster image based on the sample data and master data; determining atotal dispersion based on the dispersion within a class for each blockof the sample image and the master image and the aberration of directionwhich represents a measure of the similarity between the sample imageand the master image; determining a cross-correlation between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixel sin a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; first comparing the cross-correlationsbetween each block of the sample and master image to a referencecross-correlation; second comparing the total dispersion for each blockto a reference total dispersion; and verifying a fingerprint as that ofthe designated person based on results from the first and secondcomparisons.
 6. A method for verifying that a sample image of afingerprint is that of a designated person through comparison of sampledata, generated from the sample image, with master data, generated froma master image of the designated person's fingerprint, the master andsample image having been divided into a plurality of blocks and eachblock having been divided into a plurality of block areas, each blockarea is comprised of pixels each having an associated direction as thesample and master data, the method comprising the steps of:determining adispersion within a class of each block of the sample image based on thesample data which represents a direction of a finger characteristicwithin a block; determining a dispersion within a class of each block ofthe master image based on the master data which represents a directionof a finger characteristic within a block; determining the aberration ofdirection between blocks of the sample image and master imagerepresenting a difference in a finger characteristic between respectiveblocks of the sample image and master image based on the sample data andmaster data; determining a total dispersion based on the dispersionwithin a class for each block of the sample image and the master imageand the aberrations of direction which represents a measure of thesimilarity between the sample image and the master image; determining adistance between classes between the block areas of each block in thesample image and the master image based on a number of pixels in eachblock area of the sample image and master image associated with adirection which represents a difference in directions associated withthe pixels in a block area; calculating a mean of the distance betweenclasses between the block areas for each block of the sample and masterdata to determine a distance between classes between each block of thesample and master data; first comparing the distance between classesbetween each block of the sample and master data to a reference distancebetween classes; second comparing the total dispersion for each block toa reference total dispersion; and verifying a fingerprint as that of thedesignated person based on results from the first and secondcomparisons.
 7. A method for generating sample and master data forfingerprint verification, the method comprising the steps of:inputting agray scale image, which is one of a master image or a sample image, of afinger including a tip of said finger using an image processing portion,said gray scale image composed of pixels; outputting the gray scaleimage from said image processing portion to a data processing portion;determining a center of characteristics of the gray scale image usingthe data processing portion; taking as an area of examination that partof the gray scale image from the center of characteristics to said tipof said finger using the data processing portion; calculating for eachpixel in the area of examination density partial differentials betweensaid pixel and at least adjacent pixels in a plurality of directionsusing the data processing portion; and determining the master data orsample data in the data processing portion, the master data or sampledata being a direction of each pixel in the area of examination of themaster image or sample image, respectively, the direction of a pixelbeing a minimum density partial differential for each pixel in the areaof examination.
 8. The method of claim 7, wherein after the step oftaking, the method comprises the additional steps of:dividing the areaof examination into a plurality of blocks in the image processingportion; and determining a direction of a block for the master data orthe sample data in the image processing portion, the direction of ablock being the direction of a majority of the pixels in the block. 9.The method of claim 7, wherein after the step of taking, the methodcomprises the additional steps of:dividing the area of examination intoa plurality of blocks in the image processing portion; dividing eachblock into a plurality of block areas in the image processing portions;and determining a direction of a block area for the master data or thesample data in the image processing portion, the direction of a blockarea being the direction of a majority of the pixels in the block area.10. The method as in claim 7, wherein the step of determining the centerof characteristics comprises the steps of:determining partialdifferentials of density values between adjacent pixel sin both verticaland horizontal directions of the gray scale image; making histograms ofthe determined partial differentials of density values in the verticaland horizontal directions; and determining the center of characteristicsof the gray scale image based on the histograms.
 11. The method as inclaim 7, wherein the step of determining the center of characteristicscomprises the steps of:taking a partial differential of adjacent pixelsin the gray scale image in both a vertical, y coordinate axis, directionand horizontal, x coordinate axis, direction, summing the partialdifferentials between pixels parallel to the y-axis for each coordinateof the x-axis, determining as an x-max coordinate, a coordinate on thex-axis corresponding to a sum of partial differentials parallel to they-axis which is a maximum, summing the partial differentials betweenpixels parallel to the x-axis for each coordinate of the y-axis,determining as a y-max coordinate, a coordinate on the y-axiscorresponding to a sum of partial differentials parallel to the x-axiswhich is a maximum, and designating as the center of characteristics forthe gray scale image the x-max and y-max coordinates.
 12. A method forgenerating sample and master data for fingerprint verification, themethod comprising the steps of:inputting a gray scale image, which isone of a master image or a sample image, of a finger including a tip ofsaid finger using an image processing portion, said gray scale imagecomposed of pixels; outputting the gray scale image from said imageprocessing portion to a data processing portion; determining a center ofcharacteristics for the gray scale image using the data processingportion; taking as an area of examination an area having said center ofcharacteristics as its center using the data processing portion;calculating for each pixel in the area of examination density partialdifferentials between said pixel and at least adjacent pixels in aplurality of directions using the data processing portion; anddetermining the master data or sample data in the data processingportion, the master data or sample data being a direction of each pixelin the area of examination of the master image or sample image, thedirection of a pixel being a minimum density partial differential foreach pixel in the area of examination.
 13. The method of claim 12,wherein after the step of taking, the method comprises the additionalsteps of:dividing the area of examination into a plurality of blocks inthe image processing portions; and determining a direction of a blockfor the master data or the sample data in the image processing portion,the direction of a block being the direction of a majority of the pixelsin the block.
 14. The method of claim 12, wherein after the step oftaking, the method comprises the additional steps of:dividing the areaof examination into a plurality of blocks in the image processingportion; dividing each block into a plurality of block areas in theimage processing portion; and determining a direction of a block areafor the master data or the sample data in the image processing portion,the direction of a block area being the direction of a majority of thepixels in the block area.
 15. The method as in claim 12, wherein thestep of determining the center of characteristics comprises the stepsof:determining partial differentials of density values between adjacentpixels in both vertical and horizontal directions of the gray scaleimage; making histograms of the determined partial differentials ofdensity values in the vertical and horizontal directions; anddetermining the center of characteristics of the gray scale image basedon the histograms.
 16. The method as in claim 12, wherein the step ofdetermining the center of characteristics comprises the steps of:takinga partial differential of adjacent pixels in the gray scale image inboth a vertical, y coordinate axis, direction and horizontal, xcoordinate axis, direction, summing the partial differentials betweenpixels parallel to the y-axis for each coordinate of the x-axis,determining as an x-max coordinate, a coordinate on the x-axiscorresponding to a sum of partial differentials parallel to the y-axiswhich is a maximum, summing the partial differentials between pixelsparallel to the x-axis for each coordinate of the y-axis, determining asa y-max coordinate, a coordinate on the y-axis corresponding to a sum ofpartial differentials parallel to the x-axis which is a maximum, anddesignating as the center of characteristics for the gray scale imagethe x-max and y-max coordinates.
 17. A method for generating sample andmaster data for fingerprint verification, the method comprising thesteps of:inputting a gray scale image, which is one of a master image ora sample image, of a finger including a fillet center of said fingerusing an image processing portions, said gray scale image composed ofpixels; outputting the gray scale image from said image processingportion to a data processing portion; taking as an area of examinationan area having said center of characteristics as its center andincluding said fillet center using the data processing portion;calculating for each pixel in the area of examination density partialdifferentials between said pixel and at least adjacent pixels in aplurality of directions using the data processing portions; anddetermining the master data or sample data in the data processingportion, the master data or sample data being a direction of each pixelin the area of examination of the master image or sample image, thedirection of a pixel being a minimum density partial differential foreach pixel in the area of examination.
 18. The method of claim 17,wherein after the step of taking, the method comprises the additionalsteps of:dividing the area of examination into a plurality of blocks inthe image processing portion; and determining a direction of a block forthe master data or the sample data in the image processing portion, thedirection of a block being the direction of a majority of the pixels inthe block.
 19. The method of claim 17, wherein after the step of taking,the method comprises the additional steps of:dividing the area ofexamination into a plurality of blocks in the image processing portion;dividing each block into a plurality of block areas in the imageprocessing portion; and determining a direction of a block area for themaster data or the sample data in the image processing portion, thedirection of a block area being the direction of a majority of thepixels in the block area.
 20. The method as in claim 17, wherein thestep of determining the center of characteristics comprises the stepsof:determining partial differentials of density values between adjacentpixels in both vertical and horizontal directions of the gray scaleimage; making histograms of the determined partial differentials ofdensity values in the vertical and horizontal directions; anddetermining the center of characteristics of the gray scale image basedon the histograms.
 21. The method as in claim 17, wherein the step ofdetermining the center of characteristics comprises the steps of:takinga partial differential of adjacent pixels in the gray scale image inboth a vertical, y coordinate axis, direction and horizontal, xcoordinate axis, direction, summing the partial differentials betweenpixels parallel to the y-axis for each coordinate of the x-axis,determining as an x-max coordinate, a coordinate on the x-axiscorresponding to a sum of partial differentials parallel to the y-axiswhich is a maximum, summing the partial differentials between pixelsparallel to the x-axis for each coordinate of the y-axis, determining asa y-max coordinate, a coordinate on the y-axis corresponding to a sum ofpartial differentials parallel to the x-axis which is a maximum, anddesignating as the center of characteristics for the gray scale imagethe x-max andy-max coordinates.
 22. A method for verifying that a sampleimage of a fingerprint is that of a designated person through comparisonof sample data, generated from the sample image, with master data,generated from a master image of the designated person's fingerprint,the method comprising the steps of:inputting a gray scale image, whichis one of the master image or the sample image, of a finger including atip of said finger using an image processing portion, said gray scaleimage composed of pixels; outputting the gray scale image from saidimage processing portion to a data processing portion; determining acenter of characteristics of the gray scale image using the dataprocessing portion; taking as an area of examination that part of thegray scale image from the center of characteristics to said tip of saidfinger using the data processing portion; calculating density partialdifferentials for each pixel in the area of examination between saidpixel and at least adjacent pixels in a plurality of directions usingthe data processing portion; determining the master data or the sampledata in the data processing portion, the master data or sample databeing a direction of each pixel in the area of examination of the masterimage or sample image, respectively, the direction of a pixel being aminimum density partial differential for each pixel in the area ofexamination; and performing fingerprint verification based on the sampleand master data in the data processing portion.
 23. The method of claim22, wherein after the step of taking, the method comprises theadditional steps of:dividing the area of examination into a plurality ofblocks in the image processing portion; and determining a direction of ablock for the master data or the sample data in the image processingportion, the direction of a block being the direction of a majority ofthe pixels in the block.
 24. The method of claim 22, wherein after thestep of taking, the method comprises the additional steps of:dividingthe area of examination into a plurality of blocks in the imageprocessing portion; dividing each block into a plurality of block areasin the image processing portion; and determining a direction of a blockarea for the master data or the sample data in the image processingportion, the direction of a block area being the direction of a majorityof the pixels in the block area.
 25. The method as in claim 22, whereinthe step of determining the center of characteristics comprises thesteps of:determining partial differentials of density values betweenadjacent pixels in both vertical and horizontal directions of the grayscale image; making histograms of the determined partial differentialsof density values in the vertical and horizontal directions; anddetermining the center of characteristics of the gray scale imaged basedon the histograms.
 26. The method as in claim 22, wherein the step ofdetermining the center of characteristics comprises the steps of:takinga partial differential of adjacent pixels in the gray scale image inboth a vertical, y coordinate axis, direction and horizontal, xcoordinate axis, direction, summing the partial differentials betweenpixels parallel to the y-axis for each coordinate of the x-axis,determining as an x-max coordinate, a coordinate on the x-axiscorresponding to a sum of parallel differentials parallel to the y-axiswhich is a maximum, summing the partial differentials between pixelsparallel to the x-axis for each coordinate of the y-axis, determining asa y-max coordinate, a coordinate on the y-axis corresponding to a sum ofpartial differentials parallel to the x-axis which is a maximum, anddesignating as the center of characteristics for the gray scale imagethe x-max and y-max coordinates.
 27. The method of claim 22, wherein thefingerprint verification step comprises the following steps:dividing thearea of examination into a plurality of blocks; dividing each of theblocks into a plurality of block areas; determining a dispersion withina class of each block of the sample image based on the sample data whichrepresents a direction of a finger characteristic within a block;determining a dispersion within a class of each block of the masterimage based on the master data which represents a direction of a fingercharacteristic within a block; determining the aberration of directionbetween blocks of the sample image and master image representing adifference in a finger characteristic between respective blocks of thesample image and master image based on the sample data and master data;determining a total dispersion based on the dispersion within a classfor each block of the sample image and the master image and theaberration of direction which represents a measure of the similaritybetween the sample image and the master image; comparing the totaldispersion for each block to a reference total dispersion; and verifyinga fingerprint as that of the designated person based on results from thecomparisons.
 28. The method as in claim 27, wherein the steps ofdetermining the dispersions within a class for each block of the sampleimage and master image comprises the steps of:determining an aberrationof direction between a plurality of direction codes and a direction of apixel for each pixel in each block area which represents a difference indirection between a direction of a pixel and a direction code;determining a dispersion within a class of each block area for eachdirection code, for each direction code the determination of adispersion within a class of each block area comprises the stepsof,multiplying each pixel within a block area by a weight to form apixel product, the weight corresponding to the aberration of directionbetween the pixel and the direction code; summing the pixel products ofthe pixels in the block area; dividing the summed pixel products for theblock area by the number of pixels in the block area to determine adispersion within a class of the block area for that direction code;determining as the dispersion within a class of a block area a one ofthe dispersions within a class of the block area for each direction codewhich is a minimum; summing as a block dispersion sum, the dispersionswithin a class of a block area for a block; and dividing the block sumof a block by a number of block areas in the block to determine thedispersion within a class of the block.
 29. The method as in claim 28,wherein the step of determining the aberration of direction comprisesthe steps of:providing for each block area of the sample and masterimage a direction associated with a fingerprint characteristic of ablock area, the provided direction being the direction code for whichthe dispersion within a class of the block area for the direction codeswas a minimum; determining a direction, which is associated with afingerprint characteristic of a block, for each block in the sample andmaster image by calculating the mean of the provided directions of eachblock area within a respective block; determining an aberration ofdirection between corresponding blocks of the sample and master imagebased on the determined directions of the blocks in the sample andmaster image.
 30. The method as in claim 27, wherein the step ofdetermining the aberration of direction comprises the stepsof:determining a direction, which is associated with a fingerprintcharacteristic of a block area, for each block area in a block for boththe sample and master image based on the sample and master data;determining a direction, which is associated with a fingerprintcharacteristic of a block, for each block in the sample and master imageby calculating the mean of the directions of each block area within arespective block; determining an aberration of direction betweencorresponding blocks of the sample and master image based on thedetermined directions of the blocks in the sample and master image. 31.The method of claim 22, wherein the fingerprint verification stepcomprises the following steps:dividing the area of examination into aplurality of blocks; dividing each of the blocks into a plurality ofblock areas; determining a cross-correlation between the block areas ofeach block in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; comparing the cross-correlations betweeneach block of the sample and master image to a referencecross-correlation; and verifying a fingerprint as that of the designatedperson based on results from the comparisons.
 32. The method of claim22, wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;dividing each of the blocks into a plurality of block areas; determininga distance between classes between the block areas of each block in thesample image and the master image based on a number of pixels in eachblock area of the sample image and master image associated with adirection which represents a difference in directions associated withthe pixels in a block area; calculating a mean of the distance betweenclasses between the block areas for each block of the sample and masterdata to determine a distance between classes between each block of thesample and master data; comparing the distance between classes betweeneach block of the sample and master data to a reference distance betweenclasses; and verifying a fingerprint as that of the designated personbased on results from the comparisons.
 33. The method of claim 22,wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;dividing each of the blocks into a plurality of block areas; determininga dispersion within a class of each block of the sample image based onthe sample data which represents a direction of a finger characteristicwithin a block; determining a dispersion within a class of each block ofthe master image based on the master data which represents a directionof a finger characteristic within a block; determining the aberration ofdirection between blocks of the sample image and master imagerepresenting a difference in a finger characteristic between respectiveblocks of the sample image and master image based on the sample data andmaster data; determining a total dispersion based on the dispersionwithin a class for each block of the sample image and the master imageand the aberration of direction which represents a measure of thesimilarity between the sample image and the master image; determining across-correlation between the block areas of each block in the sampleimage and the master image based on a number of pixels in each blockarea of the sample image and master image associated with a directionwhich represents a similarity in directions associated with the pixelsin a block area; calculating a mean of the cross-correlations betweenthe block areas for each block of the sample and master image todetermine a cross-correlation between each block of the sample andmaster image; comparing the cross-correlations between each block of thesample and master image to a reference cross-correlation; secondcomparing the total dispersion for each block to a reference totaldispersion; and verifying a fingerprint as that of the designated personbased on results from the first and second comparisons.
 34. The methodof claim 22, wherein the fingerprint verification step comprises thefollowing steps:dividing the area of examination into a plurality ofblocks; dividing each of the blocks into a plurality of block areas;determining a dispersion within a class of each block of the sampleimage based on the sample data which represents a direction of a fingercharacteristic within a block; determining a dispersion within a classof each block of the master image based on the master data whichrepresents a direction of a finger characteristic within a block;determining the aberration of direction between blocks of the sampleimage and master image representing a difference in a fingercharacteristic between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; determining a distance between classes between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing the totaldispersion for each block to a reference total dispersion; and verifyinga fingerprint as that of the designated person based on results from thefirst and second comparisons.
 35. The method of claim 22, wherein thefingerprint verification step comprises the following steps:dividing thearea of examination into a plurality of blocks; dividing each of theblocks into a plurality of block areas; determining a cross-correlationbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents asimilarity in directions associated with the pixels in a block area;calculating a mean of the cross-correlations between the block areas foreach block of the sample and master image to determine across-correlation between each block of the sample and master image;determining a distance between classes between the block areas of eachblock in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing thecross-correlations between each block of the sample and master image toa reference cross-correlation; verifying a fingerprint as that of thedesignated person based on results from the first and secondcomparisons.
 36. The method of claim 22, wherein the fingerprintverification step comprises the following steps:dividing the area ofexamination into a plurality of blocks; dividing each of the blocks intoa plurality of blocks areas; determining a dispersion within a class ofeach block of the sample image based on the sample data which representsa direction of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in fingercharacteristic between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; determining a cross-correlation between the block areas ofeach block in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; determining a distance between classesbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents adifference in directions associated with the pixels in a block area;calculating a mean of the distance between classes between the blockareas for each block of the sample and master data to determine adistance between classes between each block of the sample and masterdata; first comparing the distance between classes between each block ofthe sample and master data to a reference distance between classes;second comparing the cross-correlations between each block of the sampleand master image to a reference cross-correlation; third comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the first, second and third comparisons.
 37. A method forgenerating sample and master data for fingerprint verification, themethod comprising the steps of:inputting a gray scale image, which isone of the master image or the sample image, of a finger including a tipof said finger, using an image processing portion, said gray scale imagecomposed of pixels; outputting the gray scale image from said imageprocessing portion to a data processing portion; determining a center ofcharacteristics for the gray scale image using the data processingportion; taking as an area of examination an area having said center ofcharacteristics as its center using the data processing portion;calculating density partial differentials for each pixel in the area ofexamination between said pixel and at least adjacent pixels in aplurality of directions using the data processing portion; determiningthe master data or the sample data in the data processing portion, themaster data or sample data being a direction of each pixel in the areaof examination of the master image or sample image, respectively, thedirection of a pixel being a minimum density partial differential foreach pixel in the area of examination; and performing fingerprintverification based on the sample and master data in the data processingportion.
 38. The method of claim 37, wherein after the step of taking,the method comprises the additional steps of:dividing the area ofexamination into a plurality of blocks in the image processing portion;and determining a direction of a block for the master data for thesample data in the image processing portion, the direction of a blockbeing the direction of a majority of the pixels in the block.
 39. Themethod of claim 37, wherein after the step of taking, the methodcomprises the additional steps of:dividing the area of examination intoa plurality of blocks in the image processing portion; dividing eachblock into a plurality of block areas in the image processing portion;and determining a direction of a block area for the master data or thesample data in the image processing portion, the direction of a blockarea being the direction of a majority of the pixels in the block area.40. The method as in claim 37, wherein the step of determining thecenter of characteristics comprises the steps of:determining partialdifferentials of the density values between adjacent pixels in bothvertical and horizontal directions of the gray scale image; makinghistograms of the determined partial differentials of density values inthe vertical and horizontal directions; and determining the center ofcharacteristics of the gray scale image based on the histograms.
 41. Themethod as in claim 37, wherein the step of determining the center ofcharacteristics comprises the steps of:taking a partial differential ofadjacent pixels in the gray scale image in both a vertical, y coordinateaxis, direction and horizontal, x coordinate axis, direction, summingthe partial differentials between pixels parallel to the y-axis for eachcoordinate of the x-axis, determining as an x-max coordinate, acoordinate on the x-axis corresponding to a sum of partial differentialparallel to the y-axis which is a maximum, summing the partialdifferentials between pixels parallel to the x-axis for each coordinateof the y-axis, determining as a y-max coordinate, a coordinate on they-axis corresponding to a sum of partial differentials parallel to thex-axis which is a maximum, and designating as the center ofcharacteristics for the gray scale image the x-max and y-maxcoordinates.
 42. The method of claim 37, wherein the fingerprintverification step comprises the following steps:dividing the area ofexamination into a plurality of blocks; dividing each of the blocks intoa plurality of block areas; determining a dispersion within a class ofeach block of the sample image based on the sample data which representsa direction of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in afinger characteristic between respective blocks of the sample image andmaster image based on the sample data and master data; determining atotal dispersion based on the dispersion within a class for each blockof the sample image and the master image and the aberration of directionwhich represents a measure of the similarity between the sample imageand the master image; comparing the total dispersion for each block to areference total dispersion; and verifying a fingerprint as that of thedesignated person based on result from the comparisons.
 43. The methodas in claim 42, wherein the steps of determining the dispersion within aclass for each block of the sample image and master image comprises thesteps of:determining an aberration of direction between a plurality ofdirection codes and a direction of a pixel for each pixel in each blockarea which represents a difference in direction between a direction of apixel and a direction code; determining a dispersion within a class ofeach block area for each direction code, for each direction code thedetermination of a dispersion within a class of each block areacomprises the steps of,multiplying each pixel within a block area by aweight to form a pixel product, the weight corresponding to theaberration of direction between the pixel and the direction code;summing the pixel products of the pixels in the block area; dividing thesummed pixel products for the block area by the number of pixels in theblock area to determine a dispersion within a class of the block areafor that direction code; determining as the dispersion within a class ofa block area a one of the dispersions within a class of the block areafor each direction code which is a minimum; summing as a blockdispersion sum, the dispersion within a class of a block area for ablock; and dividing the block sum of a block by a number of block areasin the block to determine the dispersion within a class of the block.44. The method as in claim 43, wherein the step of determining theaberration of direction comprises the steps of:providing for each blockarea of the sample and master image a direction associated with afingerprint characteristic of a block area, the provided direction beingthe direction code for which the dispersion within a class of the blockarea for the direction codes was a minimum; determining a direction,which is associated with a fingerprint characteristic of a block, foreach block in the sample and master image by calculating the mean of theprovided directions of each block area within a respective block;determining an aberration of direction between corresponding blocks ofthe sample and master image based on the determined directions of theblocks in the sample and master image.
 45. The method as in claim 42,wherein the step of determining the aberration of a direction comprisesthe steps of:determining a direction, which is associated with afingerprint characteristic of a block area, for each block area in ablock for both the sample and master image based on the sample andmaster data; determining a direction, which is associated with afingerprint characteristic of a block, for each block in the sample andmaster image by calculating the mean of the directions of each blockarea within a respective block; determining an aberration of directionbetween corresponding blocks of the sample and master image based on thedetermined directions of the blocks in the sample and master image. 46.The method of claim 37, wherein the fingerprint verification stepcomprises the following steps:dividing the area of examination into aplurality of blocks; dividing each of the blocks into a plurality ofblock areas; determining a cross-correlation between the blocks areas ofeach block in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; comparing the cross-correlations betweeneach block of the sample and master image to a referencecross-correlation; and verifying a fingerprint as that of the designatedperson based on results from the comparisons.
 47. The method of claim37, wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;dividing each of the blocks into a plurality of block areas; determininga distance between classes between the block areas of each block in thesample image and the master image based on a number of pixels in eachblock area of the sample image and master image associated with adirection which represents a difference in directions associated withthe pixels in a block area; calculating a mean of the distance betweenclasses between the block areas for each block of the sample and masterdata to determine a distance between classes between each block of thesample and master data; comparing the distance between classes betweeneach block of the sample and master data to a reference distance betweenclasses; and verifying a fingerprint as that of the designated personbased on results from the comparisons.
 48. The method of claim 37,wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination nito a plurality of block areas;dividing each of the blocks into a plurality of block areas; determininga dispersion within a class of each block of the sample image based onthe sample data which represents a direction of a finger characteristicwithin a block; determining a dispersion within a class of each block ofthe master image based on the master data which represents a directionof a finger characteristic within a block; determining the aberration ofdirection between blocks of the sample image and master imagerepresenting a difference in a finger characteristic between respectiveblocks of the sample image and master image based on the sample data andmaster data; determining a total dispersion based on the dispersionwithin a class for each block of the sample image and the master imageand the aberration of direction which represents a measure of thesimilarity between the sample image and the master image; determining across-correlation between the block areas of each block in the sampleimage and the master image based on a number of pixels in each blockarea of the sample image and master image associated with a directionwhich represents a similarity in directions associated with the pixelsin a block area; calculating a mean of the cross-correlations betweenthe block areas for each block of the sample and master image todetermine a cross-correlation between each block of the sample andmaster image; first comparing the cross-correlations between such blockof the sample and master image to a reference cross-correlation; secondcomparing the total dispersion for each block to a reference totaldispersion; and verifying a fingerprint as that of the designated personbased on results from the first and second comparisons.
 49. The methodof claim 37, wherein the fingerprint verification step comprises thefollowing steps:dividing the areas of examination into a plurality ofblocks; dividing each of the blocks into a plurality of block areas;determining a dispersion within a class of each block of the sampleimage based on the sample data which represents a direction of a fingercharacteristic within a block; determining a dispersion within a classof each block of the master image based on the master data whichrepresents a direction of a finger characteristic within a block;determining the aberration of direction between blocks of the sampleimage and master image representing a difference in a fingercharacteristic between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; determining a distance between classes between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing the totaldispersion for each block to a reference total dispersion; and verifyinga fingerprint as that of the designated person based on results from thefirst and second comparisons.
 50. The method of claim 37, wherein thefingerprint verification step comprises the following steps;dividing thearea of examination into a plurality of blocks; dividing each of theblocks into a plurality of block areas; determining a cross-correlationbetween the blocks areas of each block in the sample image and themaster image based on a number of pixels in each block area of thesample image and master image associated with a direction whichrepresents a similarity in directions associated with the pixels in ablock area; calculating a mean of the cross-correlations between theblock areas for each block of the sample and master image to determine across-correlation between each block of the sample and master image;determining a distance between classes between the block areas of eachblock in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing thecross-correlations between each block of the sample and master image toa reference cross-correlation; verifying a fingerprint as that of thedesignated person based on results from the first and secondcomparisons.
 51. The method of claim 37, wherein the fingerprintverification step comprises the following steps:dividing the area ofexamination into a plurality of blocks; dividing each of the blocks intoa plurality of block area; p1 determining a dispersion within a class ofeach block of the sample image based on the sample data which representsa direction of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in afinger characteristic between respective blocks of the sample image andmaster image based on the sample data and master data; determining atotal dispersion based on the dispersion within a class for each blockof the sample image and the master image and the aberration of directionwhich represents a measure of the similarity between the sample imageand the master image; determining a cross-correlation between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; determining a distance between classesbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents adifference in directions associated with the pixels in a block area;calculating a mean of the distance between classes between the blockareas for each block of the sample and master data to determine adistance between classes between each block of the sample and masterdata; first comparing the distance between classes between each block ofthe sample and master data to a reference distance between classes;second comparing the cross-correlations between each block of the sampleand master image to a reference cross-correlation; third comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the first, second and third comparisons.
 52. A method forgenerating sample and master data for fingerprint verification, themethod comprising the steps of:inputting a gray scale image, which isone of the master image or the sample image, of a finger including afillet center of said finger using an image processing portion, saidgray scale image composed of pixels; outputting the gray scale imagefrom said image processing portion to a data processing portion;determining a center of characteristics of said gray scale image usingthe data processing portion; taking as an area of examination an areahaving said center of characteristics as its center and including saidfillet center using the data processing portion; calculating densitypartial differentials for each pixel in the area of examination betweensaid pixel and at least adjacent pixels in a plurality of directionsusing the data processing portion; determining the master data or thesample data in the data processing portion, the master data or sampledata being a direction of each pixel in the area of examination of themaster image or sample image respectively, the direction of a pixelbeing a minimum density partial differential for each pixel in the areaof examination; and performing fingerprint verification based on thesample and master data in the data processing portion.
 53. The method ofclaim 52, wherein after the step of taking, the method comprises theadditional steps of:dividing the area of examination into a plurality ofblocks in the image processing portion; and determining a direction of ablock for the master data or the sample data in the image processingportion, the direction of a block being the direction of a majority ofthe pixels in the block.
 54. The method of claim 52, wherein after thestep of taking, the method comprises the additional steps of:dividingthe area of examination into a plurality of blocks in the imageprocessing portion; dividing each block into a plurality of block areasin the image processing portion; and determining a direction of a blockarea for the master data or the sample data in the image processingportion, the direction of a block area being the direction of a majorityof the pixels in the block area.
 55. The method as in claim 52, whereinthe step of determining the center of characteristics comprises thesteps of:determining partial differentials of density values betweenadjacent pixels in both vertical and horizontal directions of the grayscale image; p1 making histograms of the determined partialdifferentials of density values in the vertical and horizontaldirections; and determining the center of characteristics of the grayscale image based on the histograms.
 56. The method as in claim 52,wherein the step of determining the center of characteristics comprisesthe steps of:taking partial differential of adjacent pixels in the grayscale image in both a vertical, y coordinate axis, direction andhorizontal, x coordinate axis, direction, summing the partialdifferentials between pixels parallel to the y-axis for each coordinateof the x-axis, determining as an x-max coordinate, a coordinate on thex-axis corresponding to a sum of partial differentials parallel to they-axis which is a maximum, summing the partial differentials betweenpixels parallel to the x-axis for each coordinate of the y-axis,determining as a y-max coordinate, a coordinate on the y-axiscorresponding to a sum of partial differentials parallel to the x-axiswhich is a maximum, and designating as the center of characteristics forthe gray scale image the x-max and y-max coordinates.
 57. The method ofclaim 52, wherein the fingerprint verification step comprises thefollowing steps:dividing the area of examination into a plurality ofblocks; dividing each of the blocks into a plurality of block areas;determining a dispersion within a class of each block of the sampleimage based on the sample data which represents a direction of a fingercharacteristic within a block; determining a dispersion within a classof each block of the master image based on the master data whichrepresents a direction of a finger characteristic within a block;determining the aberration of direction between blocks of the sampleimage and master image representing a difference in a fingercharacteristics between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; comparing the total dispersion for each block of areference total dispersion; and verifying a fingerprint as that of thedesignated persons based on results from the comparisons.
 58. The methodas in claim 57, wherein the steps of determining the dispersions withina class for each block of the sample image and master image comprisesthe steps of:determining an aberration of direction between a pluralityof direction codes and a direction of a pixel for each pixel in eachblock area which represents a difference in direction between adirection of a pixel and a direction code; determining a dispersionwithin a class of each block area for each direction code, for eachdirection and code the determination of a dispersion within a class ofeach block area comprises the steps of,multiplying each pixel within ablock area by a weight to form a pixel product, the weight correspondingto the aberration of direction between the pixel and the direction code;summing the pixel products of the pixels in the block area; dividing thesummed pixel products for the block area by the number of pixels in theblock area to determine a dispersion within a class of the block areafor that direction code; determining as the dispersion within a class ofa block area a one of the dispersion within a class of the block areafor each direction code which is a minimum; summing as a blockdispersion sum, the dispersion within a class of a block area for ablock; and dividing the block sum of a block by a number of block areasin the block to determine the dispersion within a class of the block.59. The method as in claim 58, wherein the step of determining theaberration of direction comprises the steps of:providing for each blockarea of the sample and master image a direction associated with afingerprint characteristic of a block area, the provided direction beingthe direction code for which the dispersion within a class of the blockarea for the direction codes was a minimum; determining a direction,which is associated with a fingerprint characteristic of a block, foreach block in the sample and master image by calculating the mean of theprovided directions of each block area within a respective block;determining an aberration of direction between corresponding blocks ofthe sample and master image based on the determined directions of theblocks in the sample and master image.
 60. The method as in claim 57,wherein the step of determining the aberration of direction comprisesthe steps of:determining a direction, which is associated with afingerprint characteristic of a block area, for each block area in ablock for both the sample and master image based on the sample andmaster data; determining a direction, which is associated with afingerprint characteristic of a block, for each block in the sample andmaster image by calculating the mean of the directions of each blockarea within a respective block; determining an aberration of directionbetween corresponding blocks of the sample and master image based on thedetermined directions of the blocks in the sample and master image. 61.The method of claim 52, wherein the fingerprint verification stepcomprises the following steps:dividing the area of examination into aplurality of blocks; dividing each of the blocks into a plurality ofblock areas; determining a cross-correlation between the block areas ofeach block in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; comparing the cross-correlations betweeneach block of the sample and master image to a referencecross-correlation; and verifying a fingerprint as that of the designatedperson based on result from the comparisons.
 62. The method of claim 52,wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;dividing each of the blocks into a plurality of block areas; determininga distance between classes between the block areas of each block in thesample image and the master image based on a number of pixels in eachblock area of the sample image and master image associated with adirection which represents a difference in directions associated withthe pixels in a block area; calculating a mean of the distance betweenclasses between the block areas for each block of the sample and masterdata to determine a distance between classes between each block of thesample and master data; comparing the distance between classes betweeneach block of the sample and master data to a reference distance betweenclasses; and verifying a fingerprint as that of the designated personbased on results from the comparisons.
 63. The method of claim 52,wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;dividing each of the blocks into a plurality of block areas; determininga dispersion within a class of each block of the sample image based onthe sample data which represents a direction of a finger characteristicwithin a block; determining a dispersion within a class of each block ofthe master image based on the master data which represents a directionof a finger characteristic within a block; determining the aberration ofdirection between blocks of the sample image and master imagerepresenting a difference to a finger characteristic between respectiveblocks of the sample image and master image based on the sample data andmaster data; determining a total dispersion based on the dispersionwithin a class for each block of the sample image and the master imageand the aberration of direction which represents a measure of thesimilarity between the sample image and the master image; determining across-correction between the block areas of each block in the sampleimage and the master image based on a number of pixels in each blockarea of the sample image and master image associated with a directionwhich represents a similarity in directions associated with the pixelsin a block area; calculating a mean of the cross-correlation between theblock areas for each block of the sample and master image to determine across-correlation between each block of the sample and master image;first comparing the cross-correlations between each block of the sampleand master image to a reference cross-correlation; second comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the first and second comparisons.
 64. The method of claim52, wherein the fingerprint verification step comprises the followingsteps:dividing the area of examination into a plurality of blocks;`dividing each of the blocks into a plurality of block areas;determining a dispersion within a class of each block of the sampleimage based on the sample data which represents a direction of a fingercharacteristic within a block; determining a dispersion within a classof each block of the master image based on the master data whichrepresents a direction of a finger characteristic within a block;determining the aberration of direction between blocks of the sampleimage and master image representing a difference in a fingercharacteristic between respective blocks of the sample image and masterimage based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; determining a distance between classes between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing the totaldispersion for each block to a reference total dispersion; and verifyinga fingerprint as that of the designated person based on results from thefirst and second comparisons.
 65. The method of claim 52, wherein thefingerprint verification step comprises the following steps:dividing thearea of examination into a plurality of blocks; dividing each of theblocks into a plurality of block areas; determining a cross-correlationbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents asimilarity in directions associated with the pixels i a block area;calculating a mean of the cross-correlations between the block areas foreach block of the sample and master image to determine across-correlation between each block of the sample and master image;determining a distance between classes between the block areas of eachblock in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a difference in directionsassociated with the pixels in a block area; calculating a mean of thedistance between classes between the block areas for each block of thesample and master data to determine a distance between classes betweeneach block of the sample and master data; first comparing the distancebetween classes between each block of the sample and master data to areference distance between classes; second comparing thecross-correlation between each lock of the sample and master image to areference cross-correlation; verifying a fingerprint as that of thedesignated person based on results from the first and secondcomparisons.
 66. The method of claim 52, wherein the fingerprintverification step comprises the followings steps:dividing the area ofexamination into a plurality of blocks; dividing each of the blocks intoa plurality of block areas; determining a dispersion within a class ofeach block of the sample image based on the sample data which representsa direction of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in afinger characteristic between respective blocks of the sample image andmaster based on the sample data and master data; determining a totaldispersion based on the dispersion within a class for each block of thesample image and the master image and the aberration of direction whichrepresents a measure of the similarity between the sample image and themaster image; determining a cross-correlation between the block areas ofeach block in the sample image and the master image based on a number ofpixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlations between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; determining a distance between classesbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents adifference in directions associated with the pixels in a block area;calculating a mean of the distance between classes between the blockareas for each block of the sample and master data to determine adistance between classes between each block of the sample and masterdata; first comparing the distance between classes between each block ofthe sample and master data to a reference distance between classes;second comparing the cross-correlations between each block of the sampleand master image to a reference cross-correlation; third comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the first, second and third comparisons.
 67. A method forverifying that a sample image of a fingerprint is that of a designatedperson through comparison of sample data, generated from the sampleimage, with master data, generated from a master image of the designatedperson's fingerprint, the master and sample image having been dividedinto a plurality of blocks and each block having been divided into aplurality of block areas, each block area is comprised of pixels eachhaving an associated direction as the sample and master data, the methodcomprising the steps of:determining a dispersion within a class of eachblock of the sample image based on the sample data which represents adirection of a finger characteristic within a block; determining adispersion within a class of each block of the master image based on themaster data which represents a direction of a finger characteristicwithin a block; determining the aberration of direction between blocksof the sample image and master image representing a difference in afinger characteristic between respective blocks of the sample image andmaster image based on the sample data and master data; determining atotal dispersion based on the dispersion within a class for each blockof the sample image and the master image and the aberration of directionwhich represents a measure of the similarity between the sample imageand the master image; determining a cross-correlation between the blockareas of each block in the sample image and the master image based on anumber of pixels in each block area of the sample image and master imageassociated with a direction which represents a similarity in directionsassociated with the pixels in a block area; calculating a mean of thecross-correlation between the block areas for each block of the sampleand master image to determine a cross-correlation between each block ofthe sample and master image; determining a distance between classesbetween the block areas of each block in the sample image and the masterimage based on a number of pixels in each block area of the sample imageand master image associated with a direction which represents adifference in directions associated with the pixels in a block area;calculating a mean of the distance between classes between the blockareas for each block of the sample and master data to determine adistance between classes between each block of the sample and masterdata; first comparing the distance between classes between each block ofthe sample and master data to a reference distance between classes;second comparing the cross-correlation between each block of the sampleand master image to a reference cross-correlation; third comparing thetotal dispersion for each block to a reference total dispersion; andverifying a fingerprint as that of the designated person based onresults from the first, second and third comparisons.